Vegetation life cycle events can be used as distinct temporal signals to identify types of vegetation. These temporal signals contain relevant features for differentiating various kinds of vegetation. The classification of vegetation type [1]} from raw optical satellite images is done using Transformers. The performance of the Transformer architecture for vegetation classification, compared to LSTM-RNN [2]}, MS-ResNet [3]}, DuPLO [4]}, TempCNN [5]}, and random forest methods, is better for raw data. However, for pre-processed data, the performance of all methods is quite similar.
